Comprehensive comparison of programming languages ​​Julia and Python to make your decision easier!

Python has long been one of the favorite programming languages ​​​​of programmers and even other professionals. Facebook, Instagram, Spotify, Netflix, ILM, Dropbox, Yahoo! and Google all use Python in their application services.

However, as application businesses continue to increase their demand for data operations and scientific computing, people often need to rely on a more efficient data processing language. To this end, Julia was developed in 2021 by Alan Edelman, Viral B. Shah, Jeff Bezanson, and Stefan Karpinski. It can be used as an excellent alternative to Python in arithmetic coding to assist in various data processing tasks. Currently, Julia has become one of the important tools in the fields of data science, visualization, machine learning, and artificial intelligence. Features of Julia

In order to solve the problem of insufficient scientific computing capabilities of languages ​​such as Python in data processing and other application scenarios. From the beginning of development, Julia was designed to use a just-in-time (JIT) compiler built using the LLVM (Low Level Virtual Machine) compiler framework to improve runtime performance. In the best case, Julia will match or even exceed the performance of C when compiled rather than interpreted.

Julia's interactive command line is similar to Python's REPL (read-eval-print loop, read-evaluation-print loop). Various one-time use scripts and commands can be inserted instantly. At the same time, Julia has a short and efficient syntax similar to Python.

Julia can communicate directly with third-party C and Fortran libraries. You can use the PyCall module as an interface to Python programs and transfer data between Python and Julia.

Julia can generate applications and even edit their code in a way similar to Lisp (List Processing, an early free software project) language. The debugging suite, introduced in Julia 1.1, allows you to walk through the execution results of your code, inspect variables, and apply breakpoints to your code in the local REPL. For example, functions executed code by code can perform various fine-grained tasks.

Features of Python

In recent years, the classic and comprehensive Python has continued to maintain close ties with a large number of third-party programs. Although Python was not originally designed for data scientists, it has been used in this field. Because Python modules simplify the creation of special algorithms, many data scientists and machine learning professionals use Python for sentiment analysis and natural language processing (NLP).

Because it is an interpreted language, Python code does not need to go through the compilation process. It is open source and can be downloaded for free. Python can support all object-oriented concepts, including: classes, polymorphism, and encapsulation. Being inherently extensible, Python code can be written and compiled in C or C++. Python is a high-level programming language that is easy to learn and write. By importing pre-existing Python libraries, developers save development time by not having to re-enter the same data. Its line-by-line sequential execution simplifies the debugging process. At runtime, the data type of a Python variable depends on its purpose, not its declaration.

As an object-oriented interpreted programming language, Python has strong adaptability in dynamic types, advanced data structures, and dynamic binding. Programmers can Create dynamic programs. Therefore, Python is very popular and widely used.

The main weakness of Python is its slow processing speed. Of course, Python is also continuing to improve this. Its new, simplified PyPy 7.1 interpreter is much faster. In addition, Python is getting speed improvements through parallel and multi-core computing.

Comprehensive comparison between Julia and Python

1. Designed for machine learning

Python can be used to perform various application activities. The design of Julia mainly considers the load of machine learning and statistical workloads.

a) Julia has significant advantages in advanced statistics; while Python may not be able to handle all equations and matrices used in machine learning. For example, in linear algebra, "vanilla" Julia performs significantly better than "vanilla" Python.

b) Julia is better than Python in terms of non-package experience and is more suitable for machine learning calculations; of course, as an excellent language, Python can also be used through NumPy (an open source Python numerical Compute extensions) to store and process large matrices.

c) Julia's operand system is comparable to the R language operating environment used for statistical analysis and graphics. The performance disadvantage of Python in operating calculations is more obvious.

2. Speed

a) Julia's performance and speed are comparable to compiled languages ​​such as Fortran and C. However, Julia is not an interpreted language. It mainly relies on type declarations to execute programs that need to be compiled at runtime.

b) Developers can use Julia to implement high-performance applications without resorting to manual analysis and optimization methods. This is very beneficial to solving performance problems.

c) With its rich calculation and numerical features, Julia's program execution efficiency is very high. In addition, it also has multiple dispatch capabilities, which can quickly develop data types such as arrays and integers.

d) Python developers (https://skillsstreet.com/python-developer-skills/) are further improving Python's speed through optimization tools, third-party JIT compilers, and external libraries.

3. Application in data science

a) As mentioned before, Python can be used for various purposes, and data analysis is only one of the key applications.

b) Since Python includes applications, tools, and libraries that enable easy and fast data analysis and processing, it has become one of the most popular tools in data science.

c) In order to meet the growing demand for data analysis and program execution tasks, developers will apply Julia to scenarios such as scientific computing, large-scale linear algebra, machine learning, parallel and distributed computing.

d) Julia enhances the performance of Python, allowing data scientists to easily perform calculations and analysis.

4. Versatility

a) Julia enables data scientists to develop projects using various languages ​​and build them through string transmission.

b) As a very adaptable programming language, Julia can generate executable code in LaTeX, C, Python and R languages. Additionally, it is faster than Python when executing complex and large code segments.

c) In view of Julia's encapsulation disadvantage, you can call R and Python on demand through RCall and PyCall.

d) As the best choice of general-purpose and reliable language, Python can be used for web development, automation and scripting.

5. Documentation and community support

a) Every programming language is inseparable from tool support. After decades of development, Python has a large and active mutually supportive programming community. Such communities can not only provide developers with various tool support, user interfaces, and system resources through continuous improvement, but also ensure that various significant program issues receive sufficient attention.

b) As an emerging programming language, Julia's community support is still in its infancy, and various major resources and debugging tools are not currently supported. Of course, its community is becoming increasingly active.

Conclusion

To sum up, Julia has the characteristics of faster processing speed and easy code translation. Of course, it also needs further development and improvement. Although Python needs to be improved in terms of performance, it is still the language of choice for programmers, data scientists, and students. However, if you are working on a project that requires a lot of mathematical calculations, then I recommend you choose the Julia language.

Translator Introduction

Julian Chen (Julian Chen), 51CTO community editor, has more than ten years of experience in IT project implementation, is good at managing and controlling internal and external resources and risks, and focuses on spreading network and Information security knowledge and experience; continue to share cutting-edge technologies and new knowledge in the form of blog posts, special topics, and translations; often conduct information security training and teaching online and offline.

Original title: ​Julia vs. Python: Key Differences To Consider​​, author: sai kumar

The above is the detailed content of Comprehensive comparison of programming languages ​​Julia and Python to make your decision easier!. For more information, please follow other related articles on the PHP Chinese website!